EPN-V2

ACIT4030 Machine Learning for 3D Computer Vision Emneplan

Engelsk emnenavn
Machine Learning for 3D Computer Vision
Studieprogram
Master's Programme in Applied Computer and Information Technology
Omfang
10.0 stp.
Studieår
2019/2020
Programplan
Emnehistorikk

Innledning

This course will present the state of the art in algorithms for machine learning on images and 3D data. After a brief introduction to image processing and 3D geometry, we will cover topics within both supervised and unsupervised learning. The course covers classical problems like classification, segmentation, and correspondence detection. Recent work on shape and image synthesis will also be discussed. We will in particular study deep neural architectures for 2D images and 3D data such as point clouds and shape graphs. Additionally, 3D shape design with generative models will be presented.

Anbefalte forkunnskaper

We assume good background in programming, machine learning, and linear algebra. Knowledge of computer graphics and image processing is preferable, but not strictly required.

Forkunnskapskrav

No support materials for the oral examination.

Læringsutbytte

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

Innhold

  • Introduction to image processing and geometric modelling
  • Convolutional neural networks for images and graphs
  • Segmentation for images and shapes
  • Correspondences and mappings
  • Modelling, synthesis, and analysis
  • Joint embedding for images and 3D data

Arbeids- og undervisningsformer

Two internal examiners. External examiner is used periodically.

Arbeidskrav og obligatoriske aktiviteter

  • The Python programming language
  • Scientific Programming using Python
  • Automating tasks using Python
  • Utility libraries for interacting with other subsystems and frameworks
  • Git

Vurdering og eksamen

  • Individual student presentation (20 %)
  • One individually written evaluation of another student presentation (1-2 pages) (10%)
  • Individual final project report (between 25 and 35 pages) (70 %) All exams must be passed in order to pass the course.The assessment of the presentation cannot be appealed.

Hjelpemidler ved eksamen

All printed and written aids and a calculator that cannot be used to communicate with others.

Vurderingsuttrykk

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

Sensorordning

Two internal examiners. External examiner is used periodically.